c/EPA
United States
Environmental Protection
Agency
RE-Powering
Critical Infrastructure
A Study to Determine Whether RE-Powering Sites Could Meet
the Emergency Energy Needs of Wastewater Treatment Plants
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants
February 2019
Table of Contents
Introduction 1
Purpose 2
Screening Methodology Overview 2
Vulnerability Screening 5
Proximity Screening 5
Economic Screening 5
Needs Screening 6
Summary of Findings - Application of Methodology to WWTPs 7
Vulnerability and Proximity at the National Scale 7
Detailed Findings by Region 9
Discussion 14
Application to Other Types of Infrastructure 15
Conclusion 16
Appendix A: Approach for Developing Screening Criteria 18
Vulnerability Screening 18
Proximity Screening 21
Economic Screening 22
Needs Screening 24
Appendix B: Rationale and Information Sources for Proposed Threat Categorization 27
Appendix C: Datasets Used for the Analysis 36
Appendix D: Additional Summary of Findings Tables 39
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants
February 2019
Introduction
EPA's RE-Powering America's Land Initiative encourages renewable energy development on current and
formerly contaminated lands, landfills, and mine sites (RE-Powering sites) when such development is
aligned with the community's vision for the site. RE-powering can provide cleaner energy sources in
areas of high demand, while returning land to productive use. RE-Powering sites also may have
attributes that can lower renewable energy development costs and shorten development timeframes
(for example, proximity to infrastructure).
As they are often located within or near population centers, RE-Powering sites also offer opportunities
for meeting the specific energy demands of nearby off-takers, such as industrial plants, universities, and
as this analysis suggests, critical infrastructure. For the purposes of this analysis, critical infrastructure
includes assets that are key for maintaining public health and safety, such as wastewater treatment
plants (WWTPs), drinking water treatment plants, hospitals, or emergency shelters. Critical
infrastructure assets require reliable energy sources, especially in emergencies. Renewable energy in
combination with a decentralized electricity grid can make communities more resilient. Benefits of this
approach could include protection against failure of antiquated grids or, at least, isolation of specific
facilities against widespread outages, including outages associated with natural disasters and other
events (see Figure 1).
Figure 1. Illustration of RE-Powering Site Support of Infrastructure
Critical to Protecting Human Health and the Environment
MICROGRID
PUBLIC HEALTH/
ENVIRONMENT
+*
HAZARD
Scientific studies indicate that extreme weather events such as heat waves and large storms are likely to
become more frequent or intense in the future.1 Owners and end users of critical infrastructure are
recognizing the need to protect against power outages created by these more frequent and intense
1 Source: EPA, "Climate Change Indicators in the United States".
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RE Powering Critical Infrastructure: A Study to Determine
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February 2019
Creative Solutions
In Washington, D.C. the District Department of Energy
and Environment indicates that total on-site generation at
Blue Plains Advanced WWTP, "closely matches critical
process requirements." Facility managers at Blue Plains
are interested in eventually islanding the WWTP
renewable energy systems, so that they can continue to,
"operate in the event of a wider power outage."
In late 2015, the City of Santa Rosa, CA, announced a
partnership with Trane to reconfigure the city's Laguna
WWTP as a microgrid. The project, supported in part by
a $5 million grant from the California Energy Commission,
will include solar power and energy storage.
In addition, while not a WWTP, Stafford Hill Solar Farm, a
former landfill in Rutland, VT has a solar photovoltaic system
with battery storage that serves as a microgrid. The system
provides power to the city's emergency center at the high
school, offering another example of how RE-Powering sites
can support critical infrastructure needs.
document were to investigate this potential as well as demonstrate a replicable methodology for
identifying RE-Powering sites that could support renewable energy systems that could meet the
emergency energy needs of critical infrastructure. As reported in the Summary of Findings, RE-
Powering sites are widespread, often located near critical infrastructure (e.g., in industrial areas), and, in
the case of WWTPs, can frequently support energy needs for emergency operations.
While this specific analysis focuses on WWTPs, the methodology and analysis are intended to be
expandable to other types of critical infrastructure. This document describes the methodology and
presents preliminary findings and results. Limitations and potential refinements are also discussed.
Purpose
To develop and demonstrate a methodology that could be used to evaluate the potential for RE-
Powering sites to support critical infrastructure assets, including in emergency situations, and to identify
specific EPA-screened sites with the best potential for supporting wastewater treatment infrastructure.
Screening Methodology Overview
RE-Powering site data come from the August 2015 update of EPA's RE-Powering Mapper. As part of the
RE-Powering Mapper effort, EPA used screening criteria developed in collaboration with the National
Renewable Energy Laboratory (NREL) to pre-screen more than 80,000 sites for renewable energy
potential. The RE-Powering Mapper screening criteria consider site size (acreage), renewable energy
resource availability, and distances to nearest road and transmission lines.
A total of 22,299 RE-Powering sites with potential to provide at least large-scale solar or wind power
sufficient to export energy to the grid and support critical infrastructure were included in the analysis
events, in both the long and short term. The
ability to provide energy security, surety,
resiliency, and reliability through any event is
essential to protecting human health and the
environment. WWTPs protect human health
and ecosystems, and disruptions to WWTP
functioning can be devastating. The
disruption to Houston area WWTPs during
hurricane Harvey in 2017, for example,
exacerbated the pooling and stagnating of
raw sewage.
Several creative examples already exist for
supporting critical infrastructure with a
microgrid or islanded power (see Creative
Solutions text box). However, the potential
for the widespread application of RE-
Powering sites was previously unknown. Key
objectives of the analyses described in this
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RE Powering Critical Infrastructure: A Study to Determine
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February 2019
(see Figures 2 and 3)2,3 These 22,299 sites represent a total potential renewable-energy-generating
capacity of over 6,7 million megawatts (MW). Over half of these potential RE-Powering sites are in the
Northeast and Ohio Valley, whereas 71% of the renewable energy generation potential is associated
with sites in the Southwest, Northwest, and West. Sites in the Northeast and Ohio Valley tend to be
smaller and more widely distributed, while sites in western states tend to be larger, with greater
potential for renewable energy capacity at each site.
Figure 2: RE-Powering sites with at least large-scale PV and wind potential
«s
«~*
••
Hawai'i
Puerto Rico/Virgin Islands
Large Scale Solar and/or Wind RE-Powering Sites
This map is for informational purposes only.
2 For the purposes of this analysis, this includes potential RE-Powering sites classified as utility-scale PV, large-scale PV, utility-scale wind,
large-scale wind, and 1 -2 turbine wind sites. These classifications are defined in Data Documentation for Mapping and Screening Criteria for
Renewable Energy Generation Potential on EPA and State Tracked Sites RE- Powering America's Land Initiative
(https://www.ep3.qov/sites/production/files/2Q15-04/documents/repowerina mapper datadocumentation.pdf).
3 Note the higher capacity value from either wind or solar was used for the purposes of the analysis.
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RE Powering Critical Infrastructure: A Study to Determine
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February 2019
Figure 3: RE-Powering sites with at least large-scale PV and Wind potential by size
Hawaii
Puerto Rico/Virgin Islands
Large Scale RE-Powering Sites by Acreage
This map is for informational purposes only.
This analysis applies a screening approach to the Mapper data to identify RE-Powering sites that are:
• Located in areas that are likely to experience power outages - Vulnerability Screening;
• Near a WWTP - Proximity Screening;
• Economically suited for siting renewable energy - Economic Screening; and
• Most likely to meet the energy needs of the facility being supported - Needs Screening.
Below is a summary of the screening process as applied to complete the WWTP analysis. Appendices A
and B provide more detailed information about the approach for developing each of the screening
criteria. Appendix C describes the datasets used to develop the screening criteria. Appendix D contains
additional findings.
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RE Powering Critical Infrastructure: A Study to Determine
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Vulnerability Screening
Vulnerability screening identifies areas at high risk for potential extended power outages due to the
following types of hazards: hurricanes/tropical storms; tornadoes; coastal flooding, including effects of
storm surge and sea level rise (SLR); inland flooding; earthquakes; and wildfires. The approach
characterizes "vulnerability" in terms of relative threat (the potential that a hazard will cause a power
outage) and relative probability of exposure to the threat. The threat rating relates to how extreme an
event is (for example, high wind speeds in a tropical storm would be a "high" threat), while probability
represents the likelihood of a specific type of event. The probability of an area being exposed to a
hazard at different threat levels was identified using data sources that are readily available for national
geospatial analysis. Threat-probability combinations were used to create five relative vulnerability
screening categories, ranging from high threat/high probability to low threat/low probability. Appendix
A: Approach for Developing Screening Criteria - Vulnerability Screening describes the analysis approach
and provides details related to threat and probability determinations.
Proximity Screening
Proximity screening is used to identify RE-Powering sites within one mile of a critical infrastructure asset.
For the WWTP analysis, this resulted in one-to-many relationships, where multiple RE-Powering sites
were identified in proximity to a WWTP facility. This also resulted in some instances where multiple
WWTPs were in proximity to a single RE-Powering site.
Economic Screening
Economic screening is used to determine suitable sites based on whether the cost of developing
renewable energy installations at these sites would be competitive with other electricity-generating
technologies. To develop the economic screening criterion, electricity pricing data for utility service and
pricing territories where potential sites are located were compared to regional levelized cost of
electricity (LCOE) estimates developed by the U.S. Energy Information Agency (EIA). The analysis
considered two renewable energy technologies (solar and wind) at various scales as defined by the RE-
Powering program: utility-scale solar photovoltaics (PV), large-scale solar PV, utility-scale wind and
large-scale wind, and 1-2 turbine wind (see Table 1 for screening criteria, including estimated capacity
and size).4 Four categories were developed to rank the relative economic competitiveness of these
technologies, ranging from very competitive to not competitive.
4 See Data Documentation for Mapping and Screening Criteria for Renewable Energy Generation Potential on EPA and State Tracked Sites
RE-Powering America's Land Initiative for more information on the renewable energy technology and screening requirements. Note that the
screening criteria outlined in Table 1 and used to populate the RE-Powering Mapper was updated in the Fall of 2018. This analysis, however,
was completed prior to this update and reflects the criteria used in the previous version of the mapping tool, which was issued in August 2015.
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RE Powering Critical Infrastructure: A Study to Determine
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February 2019
Table 1: Screening Criteria for Solar PV and Wind
Renewable Technology
Estimated
RE Project Capacity
Range
Renewable Energy
Resource Availability
Acreage
(acres)
Distance to
Transmission
(miles)
Distance to
Graded Roads
(miles)
Solar PV
Direct Normal
(kWh/m2/day)
Utility scale
> 6.5 MW
>5.0
>40
<10
<10
Large scale
> 300 kW
>3.5
>2
<1
<1
Wind
Wind speed (m/s)
Utility scale
>10 MW
5.5 m/s at 80 m
>100
<10
<10
Large scale
> 5 MW
5.5 m/s at 80 m
>40
<10
<10
1-2 Turbine sites
> 1 MW turbine
5.5 m/s at 80 m
>2
<1
<1
Needs Screening
Needs screening refers to the estimated generation potential of RE-Powering sites relative to the energy
needs of the associated critical infrastructure. The screening considers the energy required to protect
human health, safety, and the environment in an emergency (relative to full operating power). Because
different types of infrastructure have different power requirements to maintain critical operations, the
needs screening step is infrastructure-specific.
WWTP data was collected from the 2012 EPA Clean Watersheds Needs Survey (CWNS). Facilities were
categorized according to major WWTP types, and information regarding average electric energy
intensities, including adjustment factors for levels of treatment and average flow, was used to estimate
the electric energy intensity for each WWTP in the analysis. Emergency needs consider that the RE-
Powering installation would likely only need to supply a level capable of powering critical operations
protective of human health, safety, and the environment through an emergency. Emergency power
requirements were estimated based on electric energy intensity, average daily flow rate data from the
CWNS, and an adjustment factor for emergency power load. Appendix A: Approach for Developing
Screening Criteria - Needs Screening provides more details on emergency power loads.
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RE Powering Critical Infrastructure: A Study to Determine
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Summary of Findings - Application of Methodology to WWTPs
Overall, the analysis indicates notable potential for
supporting WWTPs with renewable energy on
contaminated sites across the United States.
The results of the WWTP analysis also highlight the
importance of considering multiple criteria when
screening potential RE-Powering sites for their
ability to support energy needs for critical
infrastructure. The attractiveness of any site
depends in part on the priorities under
consideration—for example, is it more important
that a WWTP is vulnerable to outages from a high-
severity hazard event, or that the site is proximal to
more than one WWTP? Economic suitability is also
important. Even if a site can support the nearby
WWTP, it may still be difficult to develop wind or
solar if economic returns on those technologies are
not competitive.
Vulnerability and Proximity at the National
Scale
Extreme weather events can happen anywhere,
but they commonly follow patterns and occur in
specific regions of the country (see maps in Figure
4). For example, hurricanes tend to be more frequent along the eastern and gulf coast states, tornados
cluster in the mid-west (e.g., tornado alley), earth quakes have a higher probability in California along
the San Andreas Fault and the mid-west along the New Madrid Fault, flooding is prominent along
waterways and lower elevations, and wildfires can happen anywhere but tend to be more common in
the western U.S. where conditions are more favorable with fuel and lower humidity. The vulnerability
layer is "infrastructure-neutral" and allows for replicating or "scaling up" the study to include different
types of critical infrastructure.
In this case, a total of 1,563 out of 16,000 unique wastewater structures mapped in locations that rated
at least 4 ("high threat/moderate probability" or "moderate threat/high probability") for one or more
hazards. A total of 135 WWTPs in 24 states were ultimately identified (see Table 2 notes and Figures 5
and 6). WWTPs have average daily flows ranging from 0.1 million-gallons-per-day (MGD) to 812 MGD.
Over half of the WWTPs included in the analysis have an average daily flow of less than 10 MGD. As
reported in Table 2, 340 RE-Powering sites with at least large-scale capacity for wind and solar are within
one mile of WWTPs in our study.
United States
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Table 2: Summary of Evaluated WWTPs and
RE-Powering Sites
All RE Powering Sites Evaluated
81,667
Universe of Wastewater
Infrastructure from CWNS WWTPs
Evaluated
16,691
WWTPs Selected for Analysis*
135
RE Powering Sites that meet all
screening criteria**
340
* WWTPs selected for inclusion in the analysis were based
on visual verification of treatment capacity and attribute
data, indicating treatment level. Infrastructure that
appeared to merely collect or channel wastewater was
not included. Major urban areas, with clustering of RE-
Powering sites, were targeted for this sample.
**RE-Powering sites within one mile of WWTPs
(proximate screening) with a minimum vulnerability
category of 4 (vulnerability screening), a minimum
Capacity-to-Power Ratio of 1 (needs screening), and a
minimum economic sustainability of Possibly Competitive
or above (economic screening). See Appendix A: Approach
for Developing Screening Criteria provides details related
to screening categories.
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants
February 2019
Figure 4: Maps depicting areas of high hazards used for this analysis.
Tornado Path
Tropical Storm Path
Tropical Storm
— Wind Speed Greater than 74 mph
Tornado
- F3 or Greater Magnitude
Inland Flooding
Fire Hazard
Flood Hazard Areas - High Risk
Earthquake Hazard
Fire Hazard "
¦ High to Very High Fire Hazard
Ground Acceleration
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RE Powering Critical Infrastructure: A Study to Determine
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Figure 5: CWNS WWTPs Evaluated
•»
Figure 6: CWNS WWTPs Evaluated Daily Average Flow (MGD)
Daily Avsrage Flow (MGO)
# 10 - <20
# 20 - <50
# 50=100
Detailed Findings by Region
This section describes the relationship between the identified WWTPs and RE-Powering sites that
mapped to within one mile of a WWTP in four climate regions5 of the United States. A metropolitan
location was selected in each region to highlight the spatial relationship between the critical
infrastructure and the RE-Powering sites.
5 The National Centers for Environmental Information have identified nine climatically consistent regions within the contiguous United States
which are useful for putting current climate anomalies into a locational and historical perspective https://www.ncdc.noaa.aov/monitorina-
references/maps/us-climate-reaions.php).
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RE Powering Critical Infrastructure: A Study to Determine
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Northeast
The Northeast region comprises 11 states: Connecticut, Delaware, Maine, Maryland, Massachusetts,
New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. While this region is
susceptible to multiple hazards, the primary hazards of concern are tropical storms and coastal and
inland flooding. Based on the vulnerability analysis, 51 WWTPs in the Northeast region are rated at least
4 ("high threat/moderate probability" or "moderate threat/high probability") for tropical storms, 37 for
coastal flooding, and 47 for inland flooding.
Across the region, there are 135 RE-Powering sites with economically competitive solar or wind capacity
that is sufficient to support the emergency energy needs of nearby WWTPs. In addition to meeting the
emergency energy needs, there are 116 RE-Powering sites with economically competitive solar and/or
wind capacity that is sufficient to support the daily energy needs of nearby WWTPs. Figure 7 illustrates
the spatial proximity of the sites and favorable screening results.
Figure 7: Comparison of WWTPs and nearby Potential RE-
Powering Sites in the New York, New York Area
QUEENS WEST WATERFRONT DEVELOPMENT - 00505C
U STAGE II QUEENS WEST WATERFRONT DEVELOPMENT - SITE A
®STAGE II QUEENS WEST WATERFRONT DEVELOPMENT - SITE C
O QUEENS WEST (HUNTER'S POINT) PARCEL 11
I REVIEW AVENUE DEVELOPMENT II
New York (C)- Newtown Creek WPCP
> REVIEW AVENUE DEVELOPMENT I
FORMER PRATT REFINERY
-
WWTP
Emergency
Energy Needs
(MW)
© 0-1
Q >1-5
O >5-10
O >10-20
A >20
RE-Powering
Sites Near
WWTP by
Capacity (MW)
O 0-1
© >1-5
O >5-10
~ >10-20
>20
Q BP AMOCO TERMINAL-
FORMER PARAGON OIL TERMINAL (PEERLESS) O
K - GREENPOINT MGP - ENERGY CENTER
GREENPOINT
K - WILLIAMSBURG WORKS
QK-WYTHE AVE. STATION
WWTP Energy Needs
New York (C)- Newtown Creek WPCP
Daily Energy Needs -10.82 MW
Emergency Needs - 7.57 MW
Potential Capacity of Nearby RE-Powering Sites
K - GREENPOINT MGP - ENERGY CENTER - 19.56 MW
GREENPOINT-18.77 MW
STAGE II QUEENS WEST WATERFRONT DEVELOPMENT - SITE A - 5.12 MW
STAGE 11 QUEENS WEST WATERFRONT DEVELOPMENT - SITE A - 5.12 MW
FORMER PRATT REFINERY - 5.12 MW
FORMER PARAGON OIL TERMINAL (PEERLESS) - 4.35 MW
BP AMOCO TERMINAL - 3.61 MW
QUEENS WEST (HUNTER'S POINT) PARCEL 11 - 3.32 MW
K - WILLIAMSBURG WORKS - 2.92 MW
STAGE II QUEENS WEST WATERFRONT DEVELOPMENT - SITE C - 2.89 MW
QUEENS WEST WATERFRONT DEVELOPMENT - 00505C - 2.85 MW
REVIEW AVENUE DEVELOPMENT! -2.59 MW
K - WYTHE AVE. STATION -1.74 MW
REVIEW AVENUE DEVELOPMENT II -1.73 MW
Total Potential Capacity - 74.56MW
*>EPA
This map is for informational purposes oniy.
¦ Miles
0.2
0.4
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RE Powering Critical Infrastructure: A Study to Determine
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Southeast
The Southeast region, comprises Alabama, Florida, Georgia, North Carolina, South Carolina, Virginia and
the District of Columbia, This region has a high frequency of tropical storms, inland and coastal flooding.
Based on the vulnerability analysis, 25 WWTPs in the Southeast region are rated at least 4 ("high
threat/moderate probability" or "moderate threat/high probability") for tropical storms, 20 for coastal
flooding, and 23 for inland flooding.
Across the region, there are 55 RE-Powering sites with economically competitive solar or wind capacity
that is sufficient to support the emergency energy needs of nearby WWTPs. In addition to meeting the
emergency energy needs, there are 40 RE-Powering sites with economically competitive solar and/or
wind capacity that is sufficient to support the daily energy needs of nearby WWTPs. Figure 8 illustrates
the spatial proximity of the sites and favorable screening results.
Figure 8: Comparison of WWTPs by Energy Need and nearby Potential RE-Powering Sites by Capacity
in the Jacksonville, Florida Area
Q J NNEDY GENERATING STATION
WWTP
RE-Powering
Emergency
Sites Near
Energy Needs
WWTP by
(MW)
Capacity (MW)
© 0-1
o 0-1
O >1-5
O >1-5
o >5-10
o >5-10
O
tN
O
o
0 >10-20
^ >20
^ >20
i \
I
q Buckman Street STP
cma^ake\
£ KERR-MCGEE CHEMICAL CORP - JACKSONVILLE
AMERICAN CELCURE WOOD PRESERVING | j
• EASTSIDE EMPORIUM PLAZA/FORMER AMERICAN CELCURE
I FMC CORPORATION
WWTP Energy Needs
Buckman Street STP
Daily Energy Needs - 2.42 MW
Emergency Energy Needs -1.69 MW
Potential Capacity of Nearby RE-Powering Sites
JEA Kennedy Generating Station - 8.83 MW
Kerr-McGee Chemical Corp - 5.17 MW
American Celcure Wood Preserving - 0.67 MW
Eastside Emporium Plaza/Former American Celcure - 0.48 MW
FMC Corporation - 2.41 MW
Total Potential Capacity = 17.56
&EPA
This map is for informational purposes only.
I Miles
0.25
0.5
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants February 2019
Ohio Valley (Central)
The Ohio Valley region comprises Illinois, Indiana, Kentucky, Missouri, Ohio, Tennessee, and West
Virginia. This region has a high risk for damaging tornados. Based on the vulnerability analysis, seven
WWTPs in the Ohio Valley region have a high threat and high probability for tornados.
Across the region, there are 32 RE-Powering sites with economically competitive solar or wind capacity
that is sufficient to support the emergency energy needs of nearby WWTPs. In addition to meeting the
emergency energy needs, there are 30 RE-Powering sites with economically competitive solar and/or
wind capacity that is sufficient to support the daily energy needs of nearby WWTPs. Figure 9 illustrates
the spatial proximity of the sites and favorable screening results.
Figure 9: Comparison of WWTPs by Energy Need and nearby Potential RE-Powering Sites by Capacity
in the St. Louis, Missouri Area
^ UNION PACIFIC RAIL YARD
FORMER NORMAN CORPORATION
%
¦Oy.
%
\
0 Bissell Point WWTP
® GRACE HILL SETTLEMENT HOUSE
|NORTH REFUSE
" • CITY INCINERATOR & NORTH REFUSE GARAGE
iC
WWTP
Emergency
Energy Needs
RE-Powering
Sites Near
WWTP by
1
(MW)
Capacity (MW)
©
0- 1
o
0 - 1
o
>1 -5
•
>1-5
O
>5- 10
o
>5-10
o
>10-20
O
>10-20
I*
>20
O
>20
J
> PENROSE STREET
• WINTZ PROPERTIES
Q 5243-5301 HALL STREET
MALLINCKRODT LLC
WWTP Energy Needs
Bissell Point WWTP
Daily Energy Needs 4.43 MW
Emergency Energy Needs 3.1 MW
Potential Capacity of Nearby RE-Powering Sites
MALLINCKRODT LLC -10.01 MW
GRACE HILL SETTLEMENT HOUSE - 2.09 MW
FORMER NORMAN CORPORATION - 2.12 MW
CITY INCINERATOR AND NORTH REFUSE GARAGE - 3.3 MW
WINTZ PROPERTIES - 2.29 MW
UNION PACIFIC RAIL YARD - 6.73 MW
PENROSE STREET - 2.28 MW
5243-5301 HALL STREET - 1.95 MW
NORTH REFUSE - 5.06 MW
Total Potential Capacity = 35.83 MW
v>EPA
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RE Powering Critical Infrastructure: A Study to Determine
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West
The West region comprises California and Nevada and has a high potential for outages caused by
earthquake or coastal flooding. Based on the vulnerability analysis, 25 WWTPs in the Southeast region are
rated at least 4 ("high threat/moderate probability" or "moderate threat/high probability") WWTP in the
West region have a high threat and high probability for earthquakes and 19 WWTP for coastal flooding.
Across the region, there are 88 RE-Powering sites with economically competitive solar or wind capacity
that is sufficient to support the emergency energy needs of nearby WWTPs. In addition to meeting the
emergency energy needs, there are 87 RE-Powering sites with economically competitive solar and/or wind
capacity that is sufficient to support the daily energy needs of nearby WWTPs. Figure 10 illustrates the
spatial proximity of the sites and favorable screening results.
Figure 10: Comparison of WWTPs by Energy Need and nearby Potential RE-Powering Sites by Capacity in
the San Francisco Bay, California Area
CALTRANS/SSF
MAINTENANCE STATION
HOMART
DEVELOPMENT CORP.
• i
I TINMET CORPORATION/MR I
1
/
/
O'BRIEN CORPORATION A O'BRIEN CORPORATION
(CA DTSC SITE)"
EXIDECORP/
SAN FRANCISCO SVC CTR ,
"(RCRASITE)
' /
/
/
L & D EQUIPMENT CO INC
DBA LAUNDRY & DRY CLEANING EQUIPMENT CO
(j WWTP
Emergency
Energy Needs
(MW)
>5-10
RE-Powering
Sites Near
WWTP by
Capacity (MW)
o 0-1
>10-20
>20
O
>5-10
>10-20
>20
SOSFSan Bruno WWTF
i THE CROSSINGS-SAN BRUNO
03
2
| SAN FRANCISCO MUNICIPAL AIRPORT (J09CA0946) j
I
WWTP Energy Needs
SO SF San Bruno WWTF
Daily Energy Needs - 0.52 MW
Emergency Energy Needs - 0.36 MW
Potential Capacity of Nearby RE-Powering Sites
THE OBRIEN CORP (RCRA SITE) - 6 MW
THE CROSSINGS - SAN BRUNO - 5.05 MW
L AND D EQUIPMENT CO INC DBA LAUNDRY AND DRY CLEANING EQUIPMENT CO - 1.84 MW
HOMART DEVELOPMENT CORP - 4.65 MW
CALTRANS/SSF MAINTENANCE STATION -1.77 MW
TINMET CORPORATION/MR I - 5.05 MW
EX IDE CORP/SAN FRANCISCO SVC CTR - 1.75 MW
SAN FRANCISCO MUNICIPAL AIRPORT (J09CA0946) - 351.83 MW
O BR I EN CORPORATION THE (CA DTSC SITE) - 5.7 MW
Total Potential Capacity - 383.64 MW
~r
v»EPA
This map is for informational purposes only.
0.275 0.55
l Miles
1.1
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Discussion
The methodology described herein is intended to provide an initial screening of sites and infrastructure
to support broader strategic analyses—that is, to identify locations for further exploration of RE-
Powering opportunities. More in-depth and site-specific analyses would be required to understand the
feasibility of using a RE-Powering site to support the energy needs of critical infrastructure at any one
specific location.
In developing the methodology, assumptions were necessary to support a national-level analysis of the
likelihood that certain events might happen and that those events might have a catastrophic impact on
the grid. Assumptions also helped match RE-Powering site energy outputs to critical infrastructure
needs. The 1-mile proximity was decided as a reasonable distance between the RE-Powering site and
the WWTP that would facilitate the ability to microgrid the sites and manage or reduce costs associated
with building transmission lines. Other distances between RE-Powering sites and WWTPs could be
considered and explored as well. Assumptions and associated uncertainties are described in the detailed
information about the approach for developing the screening criteria (Appendix A).
The data collected for this analysis all have some limitations (Appendix C provides a list of data sources).
For example, the WWTPs were extracted from the CWNS, which contains data from a voluntary survey.
As such, the data may not represent all the possible WWTPs in a region and the accuracy of the data
cannot be guaranteed; however, available data was sufficient to apply the described methodology and
analyze the results. EPA visually verified the locations of select CWNS WWTPs by looking at the locations
on a mapping application to reduce some uncertainties.
In addition to its methodological assumptions, the analysis recognizes that extreme weather events and
natural hazards that can cause long-term power outages for critical infrastructure also create
vulnerabilities for renewable energy installations. A balance must be found between being able to
provide power in a time of need and protecting a renewable energy installation. This can limit the
capacity of a renewable energy installation to meet critical infrastructure needs during an emergency.
Two reports provide some insight into best practices for solar systems subjected to hurricane and other
severe weather events:
• Solar Under Storm: Select Best Practices for Resilient Ground Mount PV Systems with Hurricane
Exposure6 by the Rocky Mountain Institute studies similarities of solar PV systems that both
failed and survived during the 2017 hurricane season. The report discusses how incorporating
the best available engineering, design, delivery, and operational practices can increase the
reliability and survival rates from extreme wind loading.
• The U.S. Department of Energy's (DOE's) Solar Photovoltaic Systems in Hurricanes and Other
Severe Weather7 highlights how field examinations of damaged solar PV systems have revealed
important design, construction, and operational factors that greatly influence a system's
6 Solar Under Storm: Select Best Practices for Resilient Ground Mount PV Systems with Hurricane Exposure, accessed at
https://www.rmi.ora/wp-content/uploads/2018/06/lslands SolarUnderStorm Report diaitalJune122018.pdf
7 Solar Photovoltaic Systems in Hurricanes and Other Severe Weather, accessed at
https://www.enerav.aov/sites/prod/files/2018/08/f55/pv severe weather.pdf.
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survivability from a severe weather event. These events demonstrate the importance of good
operational and maintenance practices as a survivability factor, in addition to pre- and post-
storm measures that can greatly minimize equipment damage and recovery time.
State, local, and utility-level standards and codes can also affect and possibly limit the installation of
renewable energy installations in areas prone to flooding, earthquakes, and extreme storms. Most of
the information related to safety standards for renewable energy installations is administered at the
state, local, or even utility levels, as conditions vary widely across the United States.
For example, at the state level, Florida requires that rooftop solar systems meet the Florida Building Code
for permitting solar panels. The Code requires that solar panels (components and cladding) meet imposed
wind loads. California's codes for constructing solar PV systems in seismic zones are required to include,
"[calculations [that] demonstrate that the solar PV panels and associated supporting members are
designed to resist earthquake loads." Because standards and codes vary at state and local levels, it was not
possible to efficiently integrate these considerations into a methodology with national coverage.
It should also be noted that economic competitiveness is not static. State and regional policies can
change, resulting in new economic conditions and opportunities.
Application to Other Types of Infrastructure
The methodology was intentionally designed to allow for replication to incorporate other types of
critical infrastructure (e.g., hospitals, schools, emergency centers, cell towers, fire stations, natural gas
distribution centers, and others).
To replicate the analysis, datasets (including spatial data) for additional critical infrastructure types
would be required, such as emergency power needs and information about the utility servicing the
critical infrastructure. This information could then be joined to information compiled for vulnerability
screening, LCOE data, and site-associated power generation capacity. In general, the steps for replicating
the analysis for other types of infrastructure are as follows:
1. Compile hazard vulnerability data for the hazards of interest. (See Appendix C for data
sources).
2. Determine level of risk and probability to be evaluated for analysis. Screen RE-Powering
sites by overlaying them with hazard vulnerability areas.
3. Identify critical infrastructure assets that are within a predetermined distance from RE-
Powering sites identified in step 2. These will be the critical infrastructure assets of interest.
4. Gather information and assign economic competitiveness of renewable energy to the critical
infrastructure assets of interest at the desired scale.
5. Assess the emergency power needs of the remaining infrastructure assets using data
sources and methods applicable to the infrastructure type. Then compare the emergency
power needs of the infrastructure assets with the renewable energy capacity of associated
RE-Powering sites.
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Conclusion
The use of RE-Powering sites provides numerous potential benefits to communities, including returning
idle lands to productive use, providing electricity cost savings and stable electricity costs through Power
Purchase Agreements, and reducing greenhouse gas emissions. In addition, RE-Powering sites present
opportunities for powering critical infrastructure, including immediately after sudden power outages
(for example, as a result of a tropical storm). Renewable energy in combination with a decentralized
electricity grid can make communities more resilient. This approach could provide power when
antiquated grids fail or, at least, could allow for isolation of specific facilities against outages, including
those associated with natural disasters and other events.
Critical infrastructure is vital to protecting human health and the environment. In the case of WWTPs, a
system failure during a prolonged power outage can result in waste being released to rivers, streams,
lakes, or groundwater, thus impacting ecosystems as well as public health. This analysis demonstrates how
it may be possible to match the need to maintain critical WWTP infrastructure with potential RE-Powering
sites that are economically beneficial, in areas with high vulnerability to natural disasters. In addition, the
methodology, as outlined in this analysis, could be applied to other types of critical infrastructure besides
WWTPs. Consistent with the results of this analysis, it is believed that RE-Powering sites could likely meet
the specific energy demands of other types of critical infrastructure as well.
List of References
DOE, EERE (2017). Solar Photovoltaic Systems in Hurricanes and Other Severe Weather. Accessed
October 22, 2018 at
https://www.energy.gov/sites/prod/files/2018/08/f55/pv severe weather.pdf.
DOI (2016). Spatial dataset of probabilistic wildfire risk components for the conterminous United States.
Fort Collins, CO: Forest Service Research Data Archive. Accessed January 12, 2017 at
https://doi.org/10.2737/RDS-2016-0034.
EIA (2017). Electric Power Annual, with Data for 2016; Release date: December 7, 2017; Table 2.10,
Average Price of Electricity to Ultimate Customers by End-Use Sector, by State, 2016 and 2015.
Accessed January 23, 2018 at https://www.eia.gov/electricitv/annual/html/epa 02 10.html.
EIA (2017). Electric Sales, Revenue, and Average Price with Data for 2016; Release date: November 6,
2017; Table T14. Accessed January 23, 2018 at
https://www.eia.gov/electricity/sales revenue price/.
EIA (2017). Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual
Energy Outlook 2017. Accessed January 23, 2018 at
https://www.eia.gov/outlooks/aeo/electricity generation.php.
EPA (2015). Data Documentation for Mapping and Screening Criteria for Renewable Energy Generation
Potential on EPA and State Tracked Sites RE-Powering America's Land Initiative. Accessed February
28, 2017 at https://www.epa.gov/sites/production/files/2015-
04/documents/repowering mapper datadocumentation.pdf.
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Karl and Koss (1984). Regional and National Monthly, Seasonal, and Annual Temperature Weighted by
Area, 1895-1983. Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38
pp. Accessed October 19, 2018 at https://www.ncdc.noaa.gov/monitoring-references/maps/us-
climate-regions.php.
NCEI (2017). International Best Track Archive for Climate Stewardship. Accessed February 23, 2017 at
https://www.ncdc.noaa.gov/ibtracs/index.php?name=ibtracs-data.
NOAA, USGS, EPA, and Rutgers University (2017). Global and Regional Sea Level Rise Scenarios for the
United States. NOAA Technical Report NOS CO-OPS 083. Accessed February 17, 2017 at
https://tidesandcurrents.noaa.gov/publications/techrpt83 Global and Regional SLR Scenarios for
the US final.pdf.
NWS (2016). Tropical Cyclone Classification. U.S. Department of Commerce, National Oceanic and
Atmospheric Administration, National Weather Service. Accessed January 5, 2017 at
http://www.nws.noaa.gov/os/hurricane/resources/TropicalCvclonesll.pdf.
Rocky Mountain Institute (2017). Solar Under Storm: Select Best Practices for Resilient Ground Mount PV
Systems with Hurricane Exposure. Accessed October 22, 2018 at https://www.rmi.org/wp-
content/uploads/2018/06/lslands SolarUnderStorm Report digitalJunel22018.pdf.
USDA (2014). Fire Modeling Institute, USDA Forest Service, Rocky Mountain , 20141222, Wildfire Hazard
Potential (WHP) for the conterminous United States (270-m GRID), v2014 classified [whp2014_cls].
Accessed January 12, 2017 at
https://www.firelab.org/sites/default/files/images/downloads/whp2014 els faq metadata.pdf.
WRF/EPRI (2013). Electricity Use and Management in the Municipal Water Supply and Wastewater
Industries. Palo Alto, CA: Electric Power Research Institute. Accessed September 29, 2017 at
http://www.waterrf.org/Pages/Proiects.aspx?PID=4454.
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Appendix A: Approach for Developing Screening Criteria
The following appendix describes the approaches used for developing the screening criteria used in the
methodology for evaluating the potential for RE-Powering Sites to Support Critical Infrastructure.
Vulnerability Screening
The analysis used vulnerability screening to identify sites that are in areas at high risk for potential
extended power outages due to the following types of hazards:
• Hurricanes/tropical storms
• Tornadoes
• Coastal flooding, including effects of storm surge and sea level rise (SLR)
• Inland flooding
• Earthquakes
• Wildfires
Future analyses may also consider vulnerability to power outage from winter storms (a key hazard for
the electricity grid), depending on the availability of data supporting geospatial vulnerability analysis of
this type of hazard.
For the vulnerability screening, a one-mile buffer was assigned to each RE-Powering site and each buffer
area was screened to characterize hazard-specific vulnerability using the generalized risk-based
framework described herein. For areas subject to multiple threats (e.g., coastal areas subject to flooding
and hurricanes), the highest vulnerability category was used to categorize the overall vulnerability for
the area. Hazard-specific and overall relative vulnerability categories were recorded for each area to
allow for hazard-specific analysis and to show the nature of the critical vulnerability associated with
each area.8
Threat Categories
The proposed vulnerability screening approach follows a generalized risk-based framework. Each type of
event identified represents a "hazard." For purposes of this approach, the potential that a hazard will
cause a power outage is defined as the "threat," and "vulnerability" is characterized by combining a
measure of relative threat and relative probability of exposure to the threat.
To establish the draft vulnerability screening framework, the following steps were completed for each
type of hazard:
• Characterized the nature of the hazard
• Identified existing scales and research associated with each type of hazard and developed threat
thresholds corresponding to relatively high, moderate, and low threats of power outage
8 The intent of vulnerability screening is to identify critical infrastructure—rather than potential RE-Powering sites—that could be vulnerability to
more frequent and intense weather events. The approach combines elements of the proximity and vulnerability screening to define a
vulnerability layer of "areas within a mile of a RE-Powering site" that could be vulnerable to extreme weather events. The approach is
"infrastructure-neutral" in that it allows for overlaying different types of infrastructure on this vulnerability layer (including, in this case, WWTPs)
without the need for reanalysis. The overlaid critical infrastructure is linked to the underlying vulnerability rating and associated with proximal
RE-Powering site(s). See the "Replicating the Analysis" section document below for further discussion.
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• Identified data sources that are available for national geospatial analysis and could be used to
categorize the probability of an area being exposed to a hazard at different threat levels.
Table 1 summarizes the resulting threat scales and levels (thresholds) associated with each type of
hazard. The tables in Appendix C identify the rationale and information sources used to develop the
proposed threat categorization approach.
Table 1. Proposed Threat Scales and Thresholds for Vulnerability Screening
Category
Hazard(s)
Threat scales
Threat Thresholds
Hurricane/
Tropical
Storm
High winds,
inundation
Saffir-Simpson hurricane wind
scale and related tropical
cyclone categories
High: SSWS > 74 mph
Moderate: 39 mph < SSWS < 74 mph
Low: SSWS < 39 mph
Tornadoes
High winds
Enhanced Fujita Tornado Scale
High: EF > 3
Moderate: 1 < EF < 3
Low: EF = 0
Coastal
Flooding9
Inundation,
wave energy
Depth of projected inundation
based on SFHA designation,
predicted SLR, and storm
surge potential
High: DWioo ^ 3 feet
Moderate: 0 feet < DWioo < 3 feet
Low: Outside 100-year flood
zone and DW5oo ^ 0 feet
Inland
Flooding
Inundation
Depth of inundation based on
SFHA designation
High: DWioo ^3 feet
Moderate: 0 feet < DWioo < 3 feet
Low: Outside 100-year flood
zone and DW500 > 0 feet
Earthquakes
Ground
acceleration
Seismic fragility curves
High: PGA >0.48
Moderate: 0.16 < PGA < 0.48
Low: PGA <0.16
Wildfire
Fire
WHP classification scale (for
the conterminous U.S.)
High: WHP = high or very high
Moderate: WHP = moderate
Low: WHP = low or very low
Acronyms used in Table 1: DWioo = water depth, 100-year storm; DW50o = water depth, 500-year storm; EF =
Enhanced Fugita Tornado Damage Scale value; SSWS = sustained surface wind speed; PGA = Peak ground
acceleration value (as fraction of gravitational acceleration); SFHA = Special Flood Hazard Area, as designated
under the National Flood Insurance Program; SLR = sea level rise; WHP = Wildfire Hazard Potential
Probability Categories
The following relative probability categories were developed to support the relative vulnerability analysis:
• High probability: > 1% annual probability of occurrence (< 100-year return period)
• Moderate probability: > 0.2% annual probability of occurrence (< 500-year return period)
• Low probability: < 0.2% annual probability of occurrence
9 Coastal flooding has additional considerations based on storm surge and sea level rise dynamics (see the Coastal Flooding Hazard Category
in Table 1).
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These levels mirror the approach used by the National Flood Insurance Program (NFIP) for
characterizing flood risk. The approach facilitates the use of flood risk data without the need for
further statistical analysis.
Vulnerability Categories
Table 2 describes proposed vulnerability categories developed based on combinations of threat and
probability categories.
Table 2. Proposed Vulnerability Categories
Threat-
Hazard-specific Combinations
Category
Probability
Combination
Hazard
Threat Value
Annual
Probability
5
High threat/ high
Hurricane/Trop. Storm
SSWS > 74 mph
p > 1%
probability
Tornado
EF > 3
Coastal flooding*
DWioo ^ 3 feet
Inland flooding
DWioo ^ 3 feet
Earthquake
PGA > 0.48
Wildfire
WHP > high
4
High threat/
Hurricane/Trop. Storm
SSWS > 74 mph
0.2% < p < 1%
moderate
Tornado
EF > 3
probability
Coastal flooding*
DWioo ^ 3 feet
Inland flooding
DWioo ^ 3 feet
Earthquake
PGA > 0.48
Wildfire
WHP > high
Moderate
Hurricane/Trop. storm
39 mph < SSWS < 74 mph
p > 1%
threat/ high
Tornado
1< EF<3
probability
Coastal flooding*
0 feet < DWioo < 3 feet
Inland flooding
0 feet < DWioo < 3 feet
Earthquake
0.16 < PGA <0.48
Wildfire
WHP = moderate
3
Moderate
Hurricane/Trop. storm
39 mph < SSWS < 74 mph
0.2% < p < 1%
threat/ moderate
Tornado
1< EF<3
probability
Coastal flooding*
0 feet < DWioo < 3 feet
Inland flooding
0 feet < DWioo < 3 feet
Earthquake
0.16 < PGA <0.48
Wildfire
WHP = moderate
2
Moderate-to-
Hurricane/Trop. storm
SSWS > 39 mph
p < 0.2%
high threat/ low
Tornado
EF > 1
probability
Coastal flooding*
DWioo — 0 feet
Inland flooding
DWioo — 0 feet
Earthquake
PGA >0.16
Wildfire
WHP > moderate
Low threat/
Hurricane/Trop. storm
SSWS < 39 mph
p > 0.2%
moderate-to-
high probability
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Category
Threat-
Probability
Combination
Hazard-specific Combinations
Hazard
Threat Value
Annual
Probability
Tornado
m
~n
II
O
Coastal flooding*
Outside 100-yr flood
zone and DW5oo ^ 0 feet
Inland flooding
Outside 100-yr flood
zone and DW5oo ^ 0 feet
Earthquake
PGA <0.16
Wildfire
WHP < moderate
1
Low threat/ low
probability
Hurricane/Trop. storm
SSWS < 39 mph
p < 0.2%
Tornado
m
~n
II
O
Coastal flooding*
Outside 100-yr flood
zone and DW5oo ^ 0 feet
Inland flooding
Outside 100-yr flood
zone and DW5oo ^ 0 feet
Earthquake
PGA <0.16
Wildfire
WHP < moderate
* For coastal flooding, threat-probability levels are based on existing SFHA flood zone designation, which
represents analysis of flood frequency potential under existing coastal conditions, and judgement regarding
how the flood frequency designation may change by the year 2050 under the 0.5-MED and 1.0-MED SLR
scenarios described in NOAA (2017)
Acronyms used in Table 2: DWioo = water depth (feet), 100-year storm; DW50o = water depth (feet), 500-year
storm; EF = Enhanced Fugita Tornado Damage Scale value; SSWS = sustained surface wind speed; PGA = Peak
ground acceleration value (fraction of gravitational acceleration); SFHA = Special Flood Hazard Area, as
designated under the National Flood Insurance Program; SLR = sea level rise (feet); WHP = Wildfire Hazard
Potential
The same natural hazards that can cause long-term power outages for critical infrastructure could also
affect the renewable energy installations located nearby. This screening-level analysis is intended to
identify contaminated sites with renewable energy potential that are located near vulnerable critical
infrastructure and could be incorporated into a broader energy resilience strategy. More in-depth
analysis would be required prior to proceeding with a renewable energy installation in areas prone to
flooding, earthquakes, and storm paths (see the Limitations section above).
Proximity Screening
The proximity screening step identified RE-Powering sites within one mile of a WWTP, using the
generated site boundary10 for each site from the RE-Powering Mapper dataset. In many cases, this
resulted in one-to-many relationships, where multiple RE-Powering sites were identified in proximity to
a WWTP facility. This also resulted in some instances where multiple WWTPs were located in proximity
10 This is the site boundary created by using a radius that generates a site boundary equivalent to the area of the site as recorded - see Data
Documentation for Mapping and Screening Criteria for Renewable Energy Generation Potential on EPA and State Tracked Sites RE-Powering
America's Land Initiative for more detail on the RE-Powering Mapper Dataset.
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to a single RE-Powering site. The latter situation would likely be more prevalent if additional types of
critical infrastructure (e.g., drinking water treatment facilities, hospitals) were included in the analysis.
This approach does not account for existing transmission infrastructure and potential physical or other
barriers that could affect the effective distance between a RE-Powering site and a critical infrastructure
asset. This analysis is intended to provide an initial screening in support of broader strategic analyses.
More in-depth analysis would be required prior to understand the feasibility of using a RE-Powering site
to support the energy needs of critical infrastructure.
The set of sites resulting from proximity screening were further screened based on potential economic
competitiveness (economic screening) and capacity to serve emergency power needs of associated
critical infrastructure (needs screening), as described in the subsequent sections. Five renewable energy
technologies were considered in these subsequent screening analyses:11
• Utility-scale PV solar • Utility-scale wind
• Large-scale PV solar • Large-scale wind
• 1-2 turbine wind
Economic Screening
Economic screening was used to further screen sites identified in the proximity screening based on
whether the cost of developing renewable energy installations at these sites would be competitive with
other electricity-generating technologies. To assess competitiveness, the analysis compared electricity
pricing data for utility service and pricing territories where potential sites are located to regional LCOE
estimates developed by the U.S. Energy Information Agency (EIA). Sites were ranked in terms of
economic viability based on this comparison, as outlined later in this section.
The electric utility serving the area for each site was identified using Electric Retail Service Territories
data from the Department of Homeland Security's Homeland Infrastructure Foundation-Level Data.
Where available, electricity pricing data were collected for each utility associated with a site using the
EIA 2016 Utility Bundled Retail Sales - Industrial data files.12 Where pricing data were not readily
available for an area, the analysis used state-level pricing data for the industrial end-user category from
the EIA Electric Power Annual report.13
EIA estimates LCOE for technologies entering service in any year from 2018 to 2051 using its National
Energy Modeling System (NEMS).14 LCOE is an estimate of the cost of building and operating a
generating plant over its financial life and is used to evaluate the relative competiveness of different
generating technologies. EIA develops LCOE estimates for 22 different NEMS regions and provides
11 See Data Documentation for Mapping and Screening Criteria for Renewable Energy Generation Potential on EPA and State Tracked Sites
RE-Powering America's Land Initiative for more information on the renewable energy technology and screening requirements.
12 Electric Sales, Revenue, and Average Price with Data for 2016 ; Release date: November 6,2017; Table T8. Accessed January 23,2018 at
https://www.eia.gov/electricitv/sales revenue price/.
13 Electric Power Annual, with Data for 2016; Release date: December 7, 2017; Table 2.10, Average Price of Electricity to Ultimate Customers
by End-Use Sector, by State, 2016 and 2015. Accessed January 23,2018 at https://www.eia.gov/electricitv/annual/html/epa 02 10.html.
14 NEMS is a computer-based model developed by EIA to project the production, imports, conversion, consumption, and prices of energy
through 2030.
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detailed information at a national scale for the years 2019, 2022 and 2040.15 EIA recommends using
LCOE estimates for the year 2022 for evaluating the potential economic competitiveness of new
installations to account for lead time and licensing requirements.
Annual regional LCOE estimates are not publicly available but were provided on request by EIA to
support this analysis. Each site included in the dataset was associated with a NEMS region. LCOE
estimates were collected for each site for conventional wind and solar PV fixed-tilt technologies.
Consistent with ElA's recommended approach, LCOE estimates for the year 2022 were used for
economic screening.
Pricing information was compared to LCOE estimates for each site to provide an initial screening and
classify the competitiveness of developing renewable energy installations: very competitive,
competitive, possibly competitive, or not competitive using the outlined. LCOE estimates are based on
assumptions regarding capital costs, fixed and variable operations and maintenance costs, financing
costs, and utilization rate. All of these factors are affected by location-specific factors and can vary
regionally and over time, contributing to uncertainty in LCOE estimates. Ranges were used in the
economic screening criteria to account for these sources of uncertainty.
Ranking
Basis
Very competitive
LCOE < 90% of current electricity price
Competitive
90% current electricity price < LCOE < 110% current electricity price
Possibly competitive
110% current electricity price < LCOE < 120% current electricity price
Not competitive
LCOE > 120% of current electricity price
Incentives can also affect the competitiveness of a potential renewable energy installation on
contaminated lands. Such incentives include net metering, renewable portfolio standards, solar set-
asides, solar and/or wind multipliers, distributed generation, and special considerations for
development on contaminated lands. Quantitative and qualitative data were collected for states and
regions where sites in the dataset are located and could be considered in future, refined analyses of
renewable energy competitiveness. Incentives would tend to make renewable energy facilities more
competitive. The ranges outlined in the ranking table (e.g., in the criteria for the "possibly competitive"
category) were used in this screening-level analysis to account for the potential effects of incentives.
From a state and local perspective, the benefits of developing renewable energy installations extend
beyond revenue generation. They could include the benefits of infrastructure resiliency, or avoiding
costs associated with power outages. These outage-related costs could include short- and long-term
environmental contamination, deleterious effects to human health, business shutdowns and work
stoppages, and higher costs associated with restarting operations. While the benefits of resiliency and
reliability can be difficult to quantify, it is important to qualitatively consider these attributes when
evaluating the use of renewable energy to support critical infrastructure.
15 EIA (2017). Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2017. Accessed
January 23,2018 at httos://www.eia.aov/outlooks/aeo/electricitv generation.php.
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Needs Screening
Needs screening was used to further screen sites identified in the proximity screening based on whether
the potential power that the RE-Powering sites could produce would match the energy needs of the
associated WWTP. The screening considered that the RE-Powering installation would likely not need to
supply the full operating power for the facility, but instead only a level capable of powering critical
operations protective of human health, safety, and the environment through an emergency situation.
WWTP data from the EPA CWNS was used to categorize facilities according to major WWTP types
identified in the Water Research Foundation and the Electric Power Research Institute study, Electricity
Use and Management in the Municipal Water Supply and Wastewater Industries (WRF/EPRI, 2013).16
Information regarding average electric energy intensities, including adjustment factors for levels of
treatment and average flow, were used to estimate the electric energy intensity for each WWTP included
in the dataset. Emergency power requirements were estimated based on electric energy intensity, average
daily flow rate data from the CWNS, and an adjustment factor for emergency power load.
The steps completed are as follows:
1. The CWNS field PRES_FACILITY_TYPE was used to identify facilities that are treatment plants
(WWTPs) versus facilities that are collection systems only. Facilities identified as collection systems
only were removed from further consideration. The remaining dataset included 156 WWTPs.
2. The CWNS fields PRES_EFFLUENT_TREATMENT_LEVEL and DISCHARGE_METHOD were used to
categorize WWTPs in terms of treatment types listed in Table 5-5 of the WRF/EPRI (2013) document,
under the following categorization:
Table 1. Treatment Type Categories Based on CWNS Data
Effluent Treatment Level
(PRES_EFFLUENT_TREATMENT_LEVE L)
Discharge Method1
(DISCHARGE_METHOD)
Treatment Type Category
Secondary
~ Outfall to Surface Waters
~ Ocean Discharge
~ CSO Discharge
Secondary
Secondary
~ Reuse: Groundwater Recharge
~ Spray Irrigation
~ Evaporation
Secondary + No Discharge
Advanced Treatment
~ Outfall to Surface Waters
~ Ocean Discharge
Greater Than Secondary
Advanced Treatment
~ Reuse: Groundwater Recharge
~ Spray Irrigation
~ Evaporation
Greater Than Secondary + No
Discharge
Advanced Treatment
~ Reuse: Industrial
~ Reuse: Irrigation
Greater Than Secondary + Pumping
Reuse Water
16 WRF/EPRI (2013). Electricity Use and Management in the Municipal Water Supply and Wastewater Industries. Palo Alto, CA: Electric Power
Research Institute. Accessed September 29, 2017 at htto://www.waterrf.ora/Paaes/Proiects.aspx?PID=4454
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1 Only combinations of effluent treatment level and discharge method in the current dataset are included in
this table. The crosswalk can be generalized for all CWNS data if necessary.
3. Electric energy intensities for WWTPs in the dataset were estimated based on treatment-type
category and average daily flow using the following categorization, which was derived from the
electric energy intensities in Table 5-5 of the WRF/EPRI (2013) study (adjusted for flow rate using
Table 5-4 of the study).
Table 2. Estimated Average Electric Energy Intensities by Treatment Type and Flow Rate
Treatment Type Category
Electric Energy Intensity (kWh/MG) by
Average Daily Flow Rates (MG)
<5
5-<10
10 - <20
20 - <50
50 -<100
>100
Secondary
2,151
1,291
1,133
1,041
978
957
Secondary + No Discharge
2,366
1,420
1,246
1,145
1,076
1,053
Greater Than Secondary
2,809
1,785
1,638
1,527
1,439
1,428
Greater Than Secondary + No Discharge
3,090
1,964
1,802
1,680
1,583
1,571
Greater Than Secondary + Pumping Reuse Water
4,089
3,065
2,918
2,807
2,719
2,708
4. Electric energy needs for each WWTP in the dataset were calculated based on electric energy
intensity and average daily flow reported in CWNS. Emergency power load was estimated as 70% of
average electric energy needs based on Figure 5-2 from the WRF/EPRI (2013) study and the
simplifying assumption that emergency power is required for all pumping and treatment unit
processes except biosolids processing.
Tables 3 and 4 present a summary of the breakdown of treatment type and flow rate categories for the
156 facilities in the preliminary dataset using the proposed approach.
Table 3. Breakdown of Facilities by Treatment Type Category Using CWNS Data
Effluent Treatment
Level
Discharge Method
Facility Count
Treatment Type Category
Secondary
Outfall To Surface Waters
58
Secondary
Ocean Discharge
9
CSO Discharge
1
Secondary
Reuse: Groundwater Recharge
1
Secondary + No Discharge
Spray Irrigation
1
Evaporation
1
Advanced Treatment
Outfall To Surface Waters
58
Greater Than Secondary
Ocean Discharge
3
Advanced Treatment
Reuse: Groundwater Recharge
1
Greater Than Secondary + No
Discharge
Spray Irrigation
5
Evaporation
1
Advanced Treatment
Reuse: Industrial
1
Greater Than Secondary + Pumping
Reuse Water
Reuse: Irrigation
3
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Effluent Treatment
Level
Discharge Method
Facility Count
Treatment Type Category
Not "Treatment Plant"1
Discharge To Another Facility
12
N/A
CSO Discharge
1
Total
156
1 One facility in the dataset was identified as a "Treatment Plant" with "Raw Water" effluent treatment level. Upon
further review, it was determined that the facility is a collection system without treatment, not a WWTP.
Table 4. Breakdown of Facilities by Treatment Type Category and Average Daily Flow
Treatment Type Category
Flow Rate Category
Facility Count
Min Flow
Max Flow
Secondary
0
<5
28
5
<10
9
10
<20
14
20
<50
5
50
<100
5
100
no max
7
Subtotal - Secondary
68
Secondary + No discharge
0
<5
2
5
<10
0
10
<20
0
20
<50
0
50
<100
1
100
no max
0
Subtotal - Secondary + No discharge
3
Greater Than Secondary
0
<5
21
5
<10
10
10
<20
7
20
<50
9
50
<100
6
100
no max
8
Subtotal - Greater Than Secondary
61
Greater Than Secondary + No Discharge
0
<5
6
5
<10
1
10
<20
0
20
<50
0
50
<100
0
100
no max
0
Subtotal - Greater Than Secondary + No Discharge
7
Greater Than Secondary + Pumping Reuse Water
0
<5
1
5
<10
2
10
<20
1
20
<50
0
50
<100
0
100
no max
0
Subtotal - Greater Than Secondary + Pumping Reuse Water
4
Not treatment
13
Total
156
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Appendix B: Rationale and Information Sources for Proposed Threat
Categorization
Hazard category: Hurricane/Tropical Storm
Nature of hazard
High winds, inundation
Threat scales
Saffir-Simpson hurricane wind scale and related tropical cyclone categories (NWS
2016).
Threat thresholds
High: SSWS > 74 mph (categorized hurricane)
Moderate: 39 mph < SSWS < 74 mph (categorized tropical storm)
Low: SSWS < 39 mph
Threshold basis
Likelihood of outage based on NWS tropical cyclone categorization; high threat
corresponds to any categorized hurricane (on the Saffir-Simpson scale);
moderate threat corresponds to categorized tropical storm (NWS 2016).
Data sources for assessing
probability
International Best Track Archive for Climate Stewardship (IBTrACS) (NCEI 2017):
the IBTrACS project works directly with all the Regional Specialized
Meteorological Centers and other international centers and individuals to create a
global best track dataset, merging storm information from multiple centers into
one product and archiving the data for public use.
Periods used as basis for
probability assessment
• Probability based on more recent data for 1980-2015:
o The National Climate Assessment notes a substantial increase in most
measures of Atlantic hurricane activity since the early 1980s, though ability
to assess longer-term trends is limited by the quality of available data prior
to the satellite era (early 1970s) (USGCRP 2014)
o National Centers for Environmental Information (NCEI) (2017) includes data
through 2015
• Supplemental analysis of probability based on historical record from 1851 to
2015 (NCEI 2017)
Description of approach
• Calculate annual probability of hurricane (high threat event) and tropical storm
(moderate threat event) for counties intersecting the site boundary plus 1-mile
buffer using 1980-2015 period; all areas identified will fall into "high probability"
category (> 1 event/35 years —~ p > 1 %)
• For counties not identified with high probability events, calculate annual
probability of hurricane and tropical storm for counties intersecting the site
boundary plus 1-mile buffer using 1851-2015 period
• Select the highest vulnerability category associated with threat-probability
combination(s)
Acronyms:
SSWS = sustained surface wind speed
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Hazard category: Tornado
Nature of hazard
High winds
Threat scales
Enhanced Fujita Tornado Scale (NWS 2016b)
Threat thresholds
High: EF>3
Moderate: 1 < EF < 3
Low: EF = 0
Threshold basis
Likelihood of energy sector impacts as described in Colorado Energy Office
(2016), p. 190; descriptions of typical damage based on Enhanced Fujita Tornado
Scale (NOAA 2016)
Data sources for assessing
probability
Tornado tracks by F-scale as recorded in the NOAA/NCEI Storm Events
Database (NCEI 2016) and made available for geospatial analysis through
SVRGIS (NWS 2016c).
Periods used as basis for
probability assessment
• Probability based on more recent data from 1976-2015:
o The National Climate Assessment states that trends in the intensity and
frequency of tornadoes are uncertain and are being studied intensively
(USGCRP 2014)
o Tornado intensity classification has been more reliable since the advent and
adoption of the Fujita scale in the mid-1970s (Edwards et al. 2013; Grazulis
et al. 1993)
o NCEI (2016) includes data through 2015
• Supplemental analysis of probability based on historical record from 1950 to
2015 (NCEI 2016)
Description of approach
• Calculate annual probability of tornado of strength EF3 or higher (high threat
event) and tornado of EF1 or EF2 (moderate threat event) for counties
intersecting the site boundary plus 1 mile buffer using 1976-2015 period; all
areas identified will fall into "high probability" category (> 1 event/39 years —~ p
> 1%)
• For counties not identified based on the 1976-2015 period, calculate annual
probability of tornado of strength EF3 or higher and tornado of EF1 or EF2 for
counties intersecting the site boundary plus 1 mile buffer using 1950-2015
period; these areas will also fall into high probability category (> 1 event/65
years —~ p > 1 %)
• Select the highest vulnerability category associated with threat-probability
combination(s)
Acronyms:
EF = Enhanced Fugita Tornado Damage Scale value
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Hazard category: Coastal Flooding
Nature of hazard
Inundation, wave energy
Threat scales
Depth of projected inundation based on NFIP SFHA designation, predicted sea
level rise, and storm surge potential
Threat thresholds1
High: DWioo^3feet
Moderate: Ofeet 3
feet
¦ "AE" designation with a BFE of < 3 feet and predicted inundation by SLR
under 1.0-MED scenario
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Nature of hazard
Inundation, wave energy
¦ Other "A" designation and predicted inundation by SLR under 1.0-MED
scenario SLR
¦ Predicted inundation by SLR based on the 0.5-MED scenario (regardless
of current SFHA designation/status)
0 Moderate threat-high probability:
¦ "AE" designation and a BFE of < 3 feet and no SLR inundation predicted
¦ Other "A" designation and no SLR inundation predicted
¦ Shaded "X" designation (formerly Zone B) and predicted SLR inundation
under 1.0-MED scenario
0 Low threat-moderate probability:
¦ Shaded "X" designation (formerly Zone B) and no SLR predicted
¦ Not currently in NFIP-designated flood zone and predicted inundation by
SLR under 1.0-MED scenario
• Establish threat level and probability for storm surge (for buffers not assigned
high threat based on above):
0 Threat level:
¦ High threat for MOM > 3 feet
¦ Medium threat for 0 feet < MOM < 3 feet
¦ Low threat for MOM < 0 feet
0 Probability based on probability of Cat 1 hurricane (from Hurricane/ Tropical
Storm hazard method)
• Select the highest vulnerability category associated with threat-probability
combination(s)
Notes:
1 Threat levels are based on existing SFHA flood zone designation, which represents analysis of flood
frequency potential under existing coastal conditions, and judgement regarding how the flood frequency
designation may change by the year 2050 under the 0.5-MED and 1.0-MED SLR scenarios described in
NOAA (2017).
Acronyms:
DWioo = water depth (feet), 100-year storm
DW5oo = water depth (feet), 500-year storm
FIRM = Flood Insurance Rate Map
GMSL = Global mean sea level
MOM = maximum of maximum envelope of water
NFIP = National Flood Insurance Program
SFHA = Special Flood Hazard Area
SLOSH = Sea, Lake and Overland Surges from Hurricanes
SLR = sea level rise
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Hazard category: Inland Flooding
Nature of hazard
Inundation
Threat scales
Depth of inundation based on NFIP SFHA designation
Threat thresholds*
High: Dw ^ 3 feet
Moderate: 0 feet < Dw < 3 feet
Low: Outside of 100-year flood zone and DW500 ^ 0 feet
Threshold basis
Breakpoint between "shallow" flooding and deeper BFE used by NFIP (FEMA
2016) for classifying areas within 100-year floodplain
Data sources for assessing
probability
NFIP FIRM data, to establish baseline inundation hazard based on SFHA
designation
Periods used as basis for
probability assessment
Varies by site-specific factors/embedded in dataset:
• Flood return period designations are based on detailed modeling that account
for data availability and changes (e.g., land cover) affecting flood frequency
(FEMA 2017)
Description of approach
• Select highest risk SFHA designation within site boundary plus 1-mile buffer
• Establish highest threat level (based on highest risk SFHA designation):
0 High threat: "AE" designation with a BFE of > 3 feet
0 Moderate threat:
¦ "AE" designation with a BFE of < 3 feet
¦ Other "A" designation
0 Low threat: shaded "X" designation (formerly Zone B)
• Select the highest vulnerability category associated with threat-probability
combination(s), where 100-year flood corresponds to 1% annual probability
and 500-year flood corresponds to 0.2% annual probability
*Note: An inundation depth of one foot is more likely to represent a threat of power outage than the depth
of zero feet used in the screening analysis (see Boggess et al., 2014, for a discussion of standard practice for
elevating substation equipment). However, depth of inundation information is not consistently available for
NFIP-designated flood zones. The 0-foot inundation depth included in the screening method reflects this
limitation. Some areas within the intersection between a designated flood zone and the site plus 1-mile
buffer area may be exposed to a higher threat than other areas.
Acronyms:
BFE = base flood elevation
DWioo = water depth (feet), 100-year storm
DW50o = water depth (feet), 500-year storm
FIRM = Flood Insurance Rate Map
NFIP = National Flood Insurance Program
SFHA = Special Flood Hazard Area
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Hazard category: Earthquake
Nature of hazard
Ground acceleration
Threat scales
Seismic fragility curves (FEMA 2015)
Threat thresholds
High: PGA >0.48
Moderate: 0.16 < PGA <0.48
Low: PGA <0.16
Threshold basis
>75% probability of extensive damage (high threat), <25% moderate damage
(low threat), and all others moderate threat; approximate unweighted averages of
ranges for critical power network infrastructure, FEMA (2015), Figs. 8.46-8.57
Data sources for assessing
probability
Seismic Ground Motion Hazards with 10% Probability (DHS 2012): GIS
shapefiles for 10% PGA probability in 50 years (conterminous United States only)
Period used as basis for
probability assessment
1700-2006:
• USGS (2008) documents the 2008 update of the U.S. National Seismic Hazard
Maps used as the basis for DHS (2012)
• Seismic probabilistic hazard is modeled based on seismicity-derived hazard
sources, earthquakes on faults, and ground shaking resulting from these
earthquakes using a catalogue of approximately 3,350 earthquakes from 1700
through 2006 (USGS 2008)
Description of approach
• Identify highest threat level based on PGA value within site boundary plus 1-
mile buffer
• Select the highest vulnerability category associated with threat-probability
combination(s), where all threats are associated with a 0.2% (moderate)
probability (10%/50 years)
Acronyms:
PGA = Peak ground acceleration value (as fraction of gravitational acceleration)
Hazard category: Wildfire
Nature of hazard
Fire
Threat scales
Wildfire Hazard Potential (WHP) classification scale developed for the
conterminous U.S. (Dillon et al. 2015; USFS 2014)
Threat thresholds
High: WHP = high or very high
Moderate: WHP = moderate
Low: WHP = low or very low
Threshold basis
U.S. Forest Service (USFS) classification of WHP considering large and small
wildfire burn potential and resistance to control using fire suppression resources
Data sources for assessing
probability
Spatial dataset of probabilistic wildfire risk components for the conterminous
United States: National burn probability data generated for the conterminous
United States using a geospatial Fire Simulation (FSim) system developed by the
U.S. Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic
components of wildfire risk (Short et al. 2016)
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Nature of hazard
Fire
Period used as basis for
probability assessment
1970-2008:
• Burn probabilities represented in Short et al. (2016) were developed based
wildfire and weather data for the period from circa 1970 to 2008 (Finney et al.
2011)
Description of approach
• Identify all combinations of WHP classification and annual burn probability
intersecting the site boundary plus 1-mile buffer
• Select the highest vulnerability category associated with the threat-probability
combination(s)
References for Hurricane/Tropical Storm:
NCEI (2017) International Best Track Archive for Climate Stewardship. Accessed February 23, 2017, at
https://www.ncdc.noaa.gov/ibtracs/index.php?name=ibtracs-data.
NWS (2016). Tropical Cyclone Classification. U.S. Department of Commerce, National Oceanic and
Atmospheric Administration, National Weather Service. Accessed January 5, 2017, at
http://www.nws.noaa.gov/os/hurricane/resources/TropicalCvclonesll.pdf.
USGCRP (2014). Climate Change Impacts in the United States: The Third National Climate Assessment. U.S.
Global Change Research Program, 841 pp. Accessed February 16, 2017 at
http://nca2014.globalchange.gov/.
References for Tornado:
Colorado Energy Office (2016). Colorado Energy Assurance Emergency Plan. Accessed December 29, 2016 at
https://www.colorado.gov/pacific/energvoffice/energy-assurance-plan.
Edwards, R., J.G. LaDue, J.T. Feree, K. Scharfenberg, C. Maier, and W.L. Coulbourne (2013). Tornado Intensity
Estimation, Past, Present, and Future. Bulletin of the American Meteorological Society, May 2013, 641-
653. Accessed February 17, 2017 at http://iournals.ametsoc.Org/doi/full/10.1175/BAMS-D-ll-00006.l.
Grazulis, T. P., J. T. Schaefer, and R.F. Abbey Jr. (1993). Advances in tornado climatology, hazards, and risk
assessment since Tornado Symposium II. The Tornado: Its Structure, Dynamics, Prediction, and Hazard,
Geophys. Monogr., No. 79, Amer. Geophys. Union, 409-426.
NWS (2016b). Fujita Tornado Damage Scale. National Weather Service, Storm Predication Center. Accessed
December 29, 2016 at www.spc.noaa.gov/faq/tornado/f-scale.html.
NCEI (2016). Storm Events Database. Accessed January 9, 2017 at https://www.ncdc.noaa.gov/stormevents/.
NWS (2016c). Storm Prediction Center Severe Weather GIS (SVRGIS). Data available at
http://www.spc.noaa.gov/gis/svrgis/.
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References for Coastal Flooding:
FEMA (2016). National Flood Insurance Program: Flood Hazard Mapping. Federal Emergency Management
Agency. Accessed January 9, 2017 at https://www.fema.gov/national-flood-insurance-program-flood-
hazard-mapping.
FEMA (2017). Numerical Models Meeting the Minimum Requirements of the National Flood Insurance
Program. Accessed February at https://www.fema.gov/numerical-models-meeting-minimum-
requirements-national-flood-insurance-program.
NHC (2016). Sea, Lake, and Overland Surges from Hurricanes (SLOSH). National Oceanic and Atmospheric
Administration, National Hurricane Center. Accessed January 11, 2017 at
http://www.nhc.noaa.gov/surge/slosh.php.
NOAA (2016). Sea Level Rise Viewer. National Oceanic and Atmospheric Administration, Office for Coastal
Management. Accessed January 11, 2017 at https://coast.noaa.gov/digitalcoast/tools/slr.
NOAA, USGS, EPA, and Rutgers University (2017). Global and Regional Sea Level Rise Scenarios for the United
States. NOAA Technical Report NOS CO-OPS 083. Accessed February 17, 2017 at
https://tidesandcurrents.noaa.gov/publications/techrpt83 Global and Regional SLR Scenarios for the
US final.pdf.
References for Inland Flooding:
Boggess, J.M., G.W. Becker, and M.K. Mitchell (2014). Storm & Flood Hardening of Electrical Substations. IEEE
2014 T&D Conference Paper 14TD0564. Accessed February 17, 2017 at http://www.ieee-
pes.org/presentations/td2014/td2014p-00Q564.pdf.
FEMA (2016). National Flood Insurance Program: Flood Hazard Mapping. Federal Emergency Management
Agency. Accessed January 9, 2017 at https://www.fema.gov/national-flood-insurance-program-flood-
hazard-mapping.
FEMA (2017). Numerical Models Meeting the Minimum Requirements of the National Flood Insurance
Program. Accessed February at https://www.fema.gov/numerical-models-meeting-minimum-
requirements-national-flood-insurance-program.
References for Earthquake:
DHS (2012). Seismic Ground Motion Hazards with 10 Percent Probability. Department of Homeland Security
(DHS), Homeland Infrastructure Foundation-Level Data (HIFLD). Accessed February 15, 2017 at
https://hifld-dhs-gii.opendata.arcgis.com/datasets/a6802al025074246bce2dd96863cb93a 0.
FEMA (2015). Multi-hazard Loss Estimation Methodology, Earthquake Model, Hazus®-MH 2.1, Technical
Manual. U.S. Department of Homeland Security, Federal Emergency Management Agency.
USGS (2008). Documentation for the 2008 Update of the United States National Seismic Hazard Maps: U.S.
Geological Survey Open-File Report 2008-1128, 61 p. Accessed February 15, 2017 at
https://pubs.usgs.gov/of/2008/1128/.
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References for Wildfire:
Dillon, G.K., J. Menakis and F. Fay (2015). Wildland Fire Potential: A Tool for Assessing Wildfire Risk and Fuels
Management Needs. USDA Forest Service Proceedings RMRS-P-73. Accessed January 12, 2017 at
https://www.treesearch.fs.fed.us/pubs/49429.
Finney, M.A., C.W. McHugh, I.C. Grenfell, K. L. Riley, and K. C. Short (2011). A simulation of probabilistic
wildfire risk components for the continental United States. USDA Forest Service/UNL Faculty
Publications. Paper 249. Accessed .February 14, 2017 at https://www.treesearch.fs.fed.us/pubs/39312
Short, K.C., M.A. Finney, J.H. Scott, J.W. Gilbertson-Day, and I.C. Grenfell (2016). Spatial dataset of
probabilistic wildfire risk components for the conterminous United States. Data available at
https://www.fs.usda.gov/rds/archive/Product/RDS-2016-0034.
USFS (2014). Classified 2014 WHP: GIS Data and Maps. U.S. Forest Service; Fire, Fuel, and Smoke Science
Program. Accessed January 12, 2017 at https://www.firelab.org/document/classified-2014-whp-gis-data-
and-maps.
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Appendix C: Datasets Used for the Analysis
The following datasets were used in the geospatial analyses.
Screening
Type
Element
Dataset
Description
Restrictions
Source
Initial
Contaminated
Lands
RE-Powerina
Mapper
Provides detailed information for over 80,000 sites screened for
renewable energy potential.
None
EPA
Proximity
Critical
Infrastructure
Waste Water
Treatment
Plants
EPA's Clean Watersheds Needs Survey (CWNS) is an assessment of
capital investment needed nationwide for publicly-owned wastewater
collection and treatment facilities to meet the water quality goals of the
Clean Water Act. Data in the CWNS are organized by "facility." For
CWNS, the term facility used to describe a wastewater, stormwater
management, and/or decentralized wastewater management project
and location needed to address a water quality or a water quality
related-public health problem. Only "Wastewater" facilities were
evaluated for this study.
None
EPA
Vulnerability
Current and
Future Hazards
Flood Hazard
Zones
These zones are used by the federal Emergency Management Agency
(FEMA) to designate the Special Flood Hazard Area (SFHA) and for
insurance rating purposes. These data are the flood hazard areas that
are or will be depicted on the Flood Insurance Rate Map (FIRM).
None
FEMA
Historical
This layer from NOAA's Storm Prediction Center Severe Weather GIS
None
NOAA
Tornado Tracks
shows tornado tracks in the United States, Puerto Rico, and the U.S.
Virgin Islands, from 1950 to 2015.
Historical
This Historical North Atlantic and Eastern North Pacific Tropical
None
NOAA
Tropical Storm
Cyclone Tracks file contains the 6-hour center locations and intensities
Tracks
for all subtropical depressions and storms, extratropical storms, tropical
lows, waves, disturbances, depressions and storms, and all hurricanes,
from 1851 through 2008.
Sea Level Rise
This dataset depicts potential sea level rise and its associated impacts
on the nation's coastal areas. The data depict the potential inundation
of coastal areas resulting from a projected 1- to 6-foot rise in sea level
above current Mean Higher High Water (MHHW) conditions.
None
NOAA
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Screening
Type
Element
Dataset
Description
Restrictions
Source
Vulnerability
Current and
Future Hazards
Global and
Reaional Sea
Level Rise
Scenarios for
the United
States
This report highlights the linkages between scenario-based and
probabilistic projections of future sea levels for coastal-risk planning,
management of long-lived critical infrastructure, mission readiness,
and other purposes. The probabilistic projections discussed in this
report recognize the inherent dependency (conditionality) of future
global mean sea level (GMSL) rise. GMSL rise and associated RSL
change are quantified from the year 2000 through the year 2200 (on
a decadal basis to 2100 and with lower temporal frequency between
2100 and 2200).
None
NOAA
Sea, Lake and
Overland
Suraes from
Hurricanes
(SLOSH) model
This model is used to assist in a range of planning processes, risk
assessment studies, and operational decision-making. Tens of
thousands of climatology-based hypothetical tropical cyclones are
simulated in each SLOSH basin (or grid), and the potential storm
surges are calculated. Storm surge composites - Maximum Envelopes
of Water (MEOWs) and Maximum of MEOWs (MOMs) - are created to
assess and visualize storm surge risk under varying conditions.
Seismic Ground
Motion Hazards
with 10 Percent
Probabilitv
These data represent seismic hazard in the United States. The data
represent a model showing the probability that ground motion will
reach a certain level. This map layer shows peak horizontal ground
acceleration (the fastest measured change in speed, for a particle at
ground level that is moving horizontally due to an earthquake) with a
10% probability of exceedance in 50 years. Values are given in %g,
where g is acceleration due to gravity, or 9.8 meters/second2.
None
USGS17
Probabilistic
Wildfire Risk
Components
National burn probability estimates probabilistic components of wildfire
risk. It is a national-scale assessment of wildfire risk and offers a
consistent means of understanding and comparing threats to valued
resources and predicting and prioritizing investments in management
activities that mitigate those risks.
None
USDA Forest
Service18
17 Data from Homeland Security Infrastructure Program (HSIP) data. Source listed in table is the primary source referenced by HSIP.
18 Short, Karen C.; Finney, Mark A.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2016. Spatial dataset of probabilistic wildfire risk components for the conterminous United States. Fort
Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0034.
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Environmental Protection
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RE Powering Critical Infrastructure: A Study to Determine
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November 2018
Screening
Type
Element
Dataset
Description
Restrictions
Source
Vulnerability
Current and
Wildfire Hazard
This dataset is a raster geospatial product that can help to inform
None
USDA Forest
Future Hazards
Potential
evaluations of wildfire risk or prioritization of fuels management needs
across very large spatial scales (millions of acres). This dataset is not
an explicit map of wildfire threat or risk, but when paired with spatial
data depicting highly valued resources and assets such as structures
or powerlines, it can approximate relative wildfire risk to those specific
resources and assets.
Service19
Economic
Utility Service
Electric Retail
The Electric Retail Service Territories represent the service areas of
None
ElAand
Territories
Service
Territories
companies who report retail and/or commercial electricity sales to the
EIA861 Form. These companies may be investor-owned utilities,
electric cooperatives, municipalities, power marketers, etc.
Homeland
Infrastructure
Foundation -
Level Data
(HIFLD)
Levelized Cost
of Electricity
(LCOE)
Regional LCOE
values
Levelized cost of electricity (LCOE) is often cited as a convenient
summary measure of the overall competiveness of different generating
technologies. It represents the per-kilowatt-hour cost (in discounted
real dollars) of building and operating a generating plant over an
assumed financial life and duty cycle.
Unsure
EIA (special
request)
19 Fire Modeling Institute, USDA Forest Service, Rocky Mountain, 20141222, Wildfire Hazard Potential (WHP) for the conterminous United States (270-m GRID), v2014 classified [whp2014_cls].
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Environmental Protection
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants
November 2018
Appendix D: Additional Summary of Findings Tables
Table 1. Number, Location and Size of Potential RE-Powering Sites
Potential RE Powering Sites Capable of
State/Territory
(by Weather Region)
All Potential RE Powering Sites
Supporting at Least Large Scale Power
Generation
Number of
Sites
Total Acreage
Number of
Sites
Total Acreage
Total Est.
Capacity (MW)
Northeast
Connecticut
611
15,682
234
12,843
2,134
Delaware
173
18,122
115
17,272
2,895
Maine
490
25,280
110
23,353
3,542
Maryland
337
103,348
148
92,459
14,692
Massachusetts
2,507
83,434
837
76,867
12,823
New Hampshire
297
9,850
60
8,354
1,362
New Jersey
11,068
472,372
2,442
401,485
58,669
New York
3,254
525,776
1,484
508,749
81,380
Pennsylvania
6,992
1,087,964
1,995
424,607
42,424
Rhode Island
341
4,824
61
4,227
743
Vermont
307
4,165
89
3,119
560
Subtotal
26,377
2,350,818
7,575
1,573,335
221,223
Southeast and Caribbean Islands
Alabama
407
215,009
144
203,585
33,832
District of Columbia
75
79,011
17
78,966
13,161
Florida
1,582
1,399,480
345
1,366,453
227,850
Georgia
551
590,670
249
571,674
95,289
North Carolina
953
513,335
213
501,460
83,473
Puerto Rico
208
34,369
61
16,443
2,764
South Carolina
428
877,024
152
868,758
143,565
U.S. Virgin Islands
1
6
0
0
0
Virginia
5,839
380,761
1,005
320,252
52,698
Subtotal
10,044
4,089,665
2,186
3,927,591
652,631
Upper Midwest (East North Central)
Iowa
879
48,468
199
48,019
8,127
Michigan
2,786
454,169
726
448,531
72,786
Minnesota
904
877,684
333
877,028
146,107
Wisconsin
985
351,740
382
350,871
57,954
Subtotal
5,554
1,732,062
1,640
1,724,450
284,974
Ohio Valley (Central)
Illinois
6,985
297,760
1,997
290,308
48,608
Indiana
993
180,144
311
178,049
29,598
Kentucky
358
268,130
148
266,832
44,412
Missouri
1,600
712,718
304
711,375
117,926
Ohio
1,222
142,683
495
118,791
9,264
Tennessee
367
292,981
123
287,371
47,166
West Virginia
2,445
81,196
590
39,585
6,615
Subtotal
13,970
1,975,612
3,968
1,892,311
303,589
South
Arkansas
292
227,723
116
223,701
36,376
Kansas
799
217,231
257
216,636
35,619
Louisiana
501
281,811
199
278,462
46,353
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants
November 2018
State/Territory
(by Weather Region)
All Potential RE Powering Sites
Potential RE Powering Sites Capable of
Supporting at Least Large Scale Power
Generation
Number of
Sites
Total Acreage
Number of
Sites
Total Acreage
Total Est.
Capacity (MW)
Mississippi
348
23,126
100
14,256
2,377
Oklahoma
559
205,175
246
204,532
34,218
Texas
2,429
815,375
1,128
807,461
132,013
Subtotal
4,928
1,770,441
2,046
1,745,046
286,956
Northern Rockies and Plains (West North Central)
Montana
318
533,754
89
515,884
85,941
Nebraska
268
129,496
125
129,088
21,349
North Dakota
142
42,007
26
41,930
6,937
South Dakota
212
12,503
36
12,275
1,994
Wyoming
56
17,072
26
17,036
2,847
Subtotal
996
734,832
302
716,213
119,068
Southwest
Arizona
588
4,335,816
145
4,331,340
721,905
Colorado
718
622,315
163
617,688
102,993
New Mexico
202
3,338,101
95
3,337,761
556,328
Utah
276
956,761
92
955,711
159,288
Subtotal
1,784
9,252,992
495
9,242,500
1,540,513
Northwest
Idaho
284
4,127,741
103
4,116,079
686,031
Oregon
5,171
1,342,570
816
1,236,914
205,162
Washington
560
2,414,976
63
2,173,272
361,136
Subtotal
6,015
7,885,287
982
7,526,265
1,252,329
West
California
10,138
10,836,458
2,486
10,783,495
1,788,037
Nevada
440
1,002,799
116
1,000,822
166,820
Subtotal
10,578
11,839,257
2,602
11,784,317
1,954,858
Other
Alaska
176
1,364,639
2
348
24
Hawaii
1,245
789,556
501
762,451
125,351
Subtotal
1,421
2,154,195
503
762,799
125,375
Total
81,667
43,785,162
22,299
40,894,827
6,741,516
A pn* United States
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RE Powering Critical Infrastructure: A Study to Determine
Whether RE Powering Sites Could Meet the Emergency Energy Needs at Wastewater Treatment Plants
November 2018
Table 2. Summary of Potential for RE-Powering Sites to Meet Emergency Power Needs of WWTPs Identified Based on Proximity,
Vulnerability, and Needs Screening Criteria
State
WWTPs
Average Daily Flow
Emergency Power Needs
RE Capacity Available
RE Capacity to Emergency Power Ratio
Total
Max
Min
Total
Max
Min
Total
Max
Min
Max
Min
Arizona
3
9.3
6.8
1.1
0.6
0.4
0.1
9.3
6.8
1.1
>1,000
>1,000
Arkansas
1
2.2
2.2
2.2
0.1
0.1
0.1
2.2
2.2
2.2
13.7
13.7
California
23
671.8
325.0
0.9
22.7
9.1
0.1
671.8
325.0
0.9
>1,000
0.5
Connecticut
1
8.0
8.0
8.0
0.4
0.4
0.4
8.0
8.0
8.0
57.7
57.7
District of Columbia
1
370.0
370.0
370.0
15.4
15.4
15.4
370.0
370.0
370.0
1.5
1.5
Florida
11
103.6
50.5
0.1
6.1
2.1
0.0
103.6
50.5
0.1
>1,000
0.3
Georgia
2
7.1
5.6
1.5
0.3
0.2
0.1
7.1
5.6
1.5
926.2
3.0
Illinois
4
844.7
812.0
1.7
35.4
33.8
0.1
844.7
812.0
1.7
485.8
46.6
Maine
1
2.0
2.0
2.0
0.1
0.1
0.1
2.0
2.0
2.0
14.5
14.5
Maryland
1
1.4
1.4
1.4
0.1
0.1
0.1
1.4
1.4
1.4
>1,000
>1,000
Massachusetts
17
107.6
25.8
0.9
4.6
0.8
0.1
107.6
25.8
0.9
>1,000
5.8
Michigan
2
741.5
660.5
81.0
30.9
27.5
3.4
741.5
660.5
81.0
>1,000
11.9
Mississippi
3
14.9
6.0
3.8
0.7
0.3
0.2
14.9
6.0
3.8
870.3
15.8
Missouri
3
239.0
114.0
14.0
6.8
3.2
0.5
239.0
114.0
14.0
40.3
3.0
New Hampshire
1
12.0
12.0
12.0
0.4
0.4
0.4
12.0
12.0
12.0
3.8
3.8
New Jersey
7
286.6
177.6
0.2
11.3
7.4
0.0
286.6
177.6
0.2
>1,000
0.9
New York
7
615.7
271.3
1.0
21.7
7.6
0.1
615.7
271.3
1.0
205.8
2.2
North Carolina
3
19.0
14.0
1.9
1.1
0.7
0.2
19.0
14.0
1.9
44.2
1.9
Pennsylvania
3
201.6
196.7
1.7
5.9
5.5
0.1
201.6
196.7
1.7
>1,000
3.3
Texas
8
321.4
152.0
1.2
13.9
6.3
0.1
321.4
152.0
1.2
>1,000
3.5
Vermont
1
1.7
1.7
1.7
0.1
0.1
0.1
1.7
1.7
1.7
4.2
4.2
Washington
1
2.4
2.4
2.4
0.2
0.2
0.2
2.4
2.4
2.4
>1,000
>1,000
Total
104
4,584
812.0
0.1
178.8
33.8
0.0
4,584
812.0
0.1
>1,000
0.3
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Environmental Protection
I M \ Agency
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